Robot to human feedback

ABSTRACT

Example implementations may relate to a robotic system configured to provide feedback. In particular, the robotic system may determine a model of an environment in which the robotic system is operating. Based on this model, the robotic system may then determine one or more of a state or intended operation of the robotic system. Then, based one or more of the state or the intended operation, the robotic system may select one of one or more of the following to represent one or more of the state or the intended operation: visual feedback, auditory feedback, and one or more movements. Based on the selection, the robotic system may then engage in one or more of the visual feedback, the auditory feedback, and the one or more movements.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims priority to U.S. Provisional patentapplication Ser. No. 62/041,299 filed on Aug. 25, 2014 and entitled“Robot to Human Feedback,” the entire contents of which are hereinincorporated by reference.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

Robotic systems may be used for applications involving materialhandling, welding, assembly, dispensing, and companionship, amongothers. Over time, the manner in which these robotic systems operate isbecoming more intelligent, more efficient, and more intuitive. Asrobotic systems become increasingly prevalent in numerous aspects ofmodern life, the need for robotic systems that can properly interactwith humans and the environment becomes apparent. Therefore, a demandfor such robotic systems has helped open up a field of innovation insensing techniques, feedback modes, as well as component design andassembly.

SUMMARY

Example implementations may relate to methods and systems for a roboticsystem to provide feedback to a human. A robotic system may beconfigured to provide feedback to a human by, for instance, operating avisual indicator, operating an auditory indicator, using gestures,and/or sending notifications to a computing device. In particular, therobotic system may evaluate the surroundings in which the robotic systemis located and may determine a performance metric based on such anevaluation. The performance metric may be associated, for example, witha level of safety of a situation in the surroundings and/or with a taskthat the robotic system carrying out in the surroundings. Based on sucha metric, the robotic system may select an operating mode that includesproviding the appropriate feedback based on the situation in thesurroundings.

In one aspect, a method is provided. The method involves determining, bya robotic system, a model of an environment in which the robotic systemis operating. The method also involves determining, by the roboticsystem, one or more of a state or intended operation of the roboticsystem based at least in part on the model of the environment. Themethod additionally involves, based at least in part on one or more ofthe state or the intended operation, making a selection, by the roboticsystem, of one or more of visual feedback to represent one or more ofthe state or the intended operation, auditory feedback to represent oneor more of the state or the intended operation, and one or moremovements to represent one or more of the state or the intendedoperation. The method further involves, based at least in part on theselection, engaging, by the robotic system, in one or more of the visualfeedback to represent one or more of the state or the intendedoperation, the auditory feedback to represent one or more of the stateor the intended operation, and the one or more movements to representone or more of the state or the intended operation.

In another aspect, a robotic system is provided. The robotic systemincludes one or more processors, a non-transitory computer readablemedium, and program instructions stored on the non-transitory computerreadable medium and executable by the one or more processors todetermine a model of an environment in which the robotic system isoperating. The instructions are also executable to determine one or moreof a state or intended operation of the robotic system based at least inpart on the model of the environment. The instructions are additionallyexecutable to, based at least in part on one or more of the state or theintended operation, make a selection of one or more of visual feedbackto represent one or more of the state or the intended operation,auditory feedback to represent one or more of the state or the intendedoperation, and one or more movements to represent one or more of thestate or the intended operation. The instructions are further executableto, based at least in part on the selection, engage in one or more ofthe visual feedback to represent one or more of the state or theintended operation, the auditory feedback to represent one or more ofthe state or the intended operation, and the one or more movements torepresent one or more of the state or the intended operation.

In yet another aspect, a non-transitory computer readable medium isprovided. The non-transitory computer readable medium has stored thereininstructions executable by one or more processors to cause a roboticsystem to perform functions. The functions include determining a modelof an environment in which the robotic system is operating. Thefunctions also include determining one or more of a state or intendedoperation of the robotic system based at least in part on the model ofthe environment. The functions additionally include, based at least inpart on one or more of the state or the intended operation, making aselection of one or more of visual feedback to represent one or more ofthe state or the intended operation, auditory feedback to represent oneor more of the state or the intended operation, and one or moremovements to represent one or more of the state or the intendedoperation. The functions further include, based at least in part on theselection, engaging in one or more of the visual feedback to representone or more of the state or the intended operation, the auditoryfeedback to represent one or more of the state or the intendedoperation, and the one or more movements to represent one or more of thestate or the intended operation.

In yet another aspect, a system is provided. The system may includemeans for determining a model of an environment in which the roboticsystem is operating. The system may also include means for determiningone or more of a state or intended operation of the robotic system basedat least in part on the model of the environment. The system mayadditionally include means for, based at least in part on one or more ofthe state or the intended operation, making a selection of one or moreof visual feedback to represent one or more of the state or the intendedoperation, auditory feedback to represent one or more of the state orthe intended operation, and one or more movements to represent one ormore of the state or the intended operation. The system may furtherinclude means for, based at least in part on the selection, engaging inone or more of the visual feedback to represent one or more of the stateor the intended operation, the auditory feedback to represent one ormore of the state or the intended operation, and the one or moremovements to represent one or more of the state or the intendedoperation.

These as well as other aspects, advantages, and alternatives will becomeapparent to those of ordinary skill in the art by reading the followingdetailed description, with reference where appropriate to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example robotic system, according to an exampleimplementation.

FIGS. 2A-2F illustrate graphical examples of a robot, according to anexample implementation.

FIG. 3 is a flowchart illustrating a method for robot to human feedback,according to an example implementation.

FIGS. 4A-4D illustrate an example scenario of robot to human feedback,according to an example implementation.

FIGS. 5A-5E illustrate another example scenario of robot to humanfeedback, according to an example implementation.

DETAILED DESCRIPTION

Example methods and systems are described herein. It should beunderstood that the words “example,” “exemplary,” and “illustrative” areused herein to mean “serving as an example, instance, or illustration.”Any implementation or feature described herein as being an “example,”being “exemplary,” or being “illustrative” is not necessarily to beconstrued as preferred or advantageous over other implementations orfeatures. The example implementations described herein are not meant tobe limiting. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated herein.

I. Overview

According to various implementations, described herein are systems andmethods involving a robotic system configured to provide feedback tohumans. Generally, a robotic system can include a plurality of sensorsthat the robotic system may use to gather information (i.e., based onsensor data) about the environment in which the robotic system isoperating. In particular, the robotic system can obtain the sensor dataand interpret the data into an understanding of objects, human faces,and gestures, as well as various situations in the environment, amongother possibilities.

The obtained information may influence a state of the robotic system(e.g., a current model or interpretation of the environment), intention(e.g., a planned behavior or action of the robotic system), and/oroverall safety of interaction (e.g., caution while carrying out a task).The robotic system may then use various modes to convey the state,intention, and overall safety of interaction. In this manner, therobotic system may provide feedback about a task and progress of thetask as well as about safety concerns associated with the task and/orthe environment.

II. Example Robotic Systems

Referring now to the figures, FIG. 1 illustrates an example roboticsystem 100. A robotic system 100 may include any computing device(s)that have an actuation capability (e.g., electromechanicalcapabilities). In particular, the robotic system 100 may containcomputer hardware, such as a processor 102, memory or storage 104,sensors 106, and mechanical actuators 108. For example, a robotcontroller (e.g., processor 102, a computing system, and sensors 106)may be custom designed for the robotic system 100. Note that the roboticsystem may also be referred to as a robotic device, a robot client, anda robot, among other possibilities.

In an example implementation, the storage 104 may be used for compilingdata from various sensors 106 of the robotic system 100 and storingprogram instructions. The processor 102 may be coupled to the storage104 and may be configured to control the robotic system 100 based on theprogram instructions. The processor 102 may also be able to interpretdata from the various sensors 106 on the robotic system 100.

Example sensors 106 may include a gyroscope or an accelerometer tomeasure movement of the robot system. The sensors 106 may also includeany of Global Positioning System (GPS) receivers, sonar, opticalsensors, biosensors, Radio Frequency identification (RFID) systems, NearField Communication (NFC) chip, wireless sensors, and/or compasses.Other sensors 106 may further include smoke sensors, light sensors,radio sensors, microphones, speakers, radar, capacitive sensors, touchsensors, cameras (e.g., color cameras, grayscale cameras, and/orinfrared cameras), depth sensors (e.g., RGB-D, laser, structured-light,and/or a time-of-flight camera), motion detectors (e.g., an inertialmeasurement unit (IMU), and/or foot step or wheel odometry), and/orrange sensors (e.g., ultrasonic and/or infrared), among others.

The robotic system 100 may also have components or devices that allowthe robotic system 100 to interact with its environment (i.e.,surroundings). For example, the robotic system 100 may have mechanicalactuators 108, such as motors, wheels, movable arms, etc., that enablethe robotic system 100 to move or interact with the environment in orderto carry out various tasks.

In some examples, various sensors and devices on the robotic system 100may be modules. Different modules may be added or removed from therobotic system 100 depending on requirements. For example, in a lowpower situation, the robotic system 100 may have fewer modules to reducepower usages. However, additional sensors may be added as needed. Toincrease an amount of data the robotic system 100 may be able tocollect, additional sensors may be added, for example. Note that any ofthe modules may be interconnected, and/or may communicate to receivedata or instructions from each other so as to provide a specific outputor functionality for the robotic system 100.

In some implementations, the robotic system 100 may have a link by whichthe link can access cloud servers, communicate with other roboticsystems, and/or communicate with other computing devices. A wired linkmay include, for example, a parallel bus or a serial bus such as aUniversal Serial Bus (USB). A wireless link may include, for example,Bluetooth, IEEE 802.11 (IEEE 802.11 may refer to IEEE 802.11-2007, IEEE802.11n-2009, or any other IEEE 802.11 revision), Cellular (such as GSM,GPRS, CDMA, UMTS, EV-DO, WiMAX, HSPDA, or LTE), or Zigbee, among otherpossibilities. Furthermore, the robotic system 100 may be configured touse multiple wired and/or wireless protocols, such as “3G” or “4G” dataconnectivity using a cellular communication protocol (e.g., CDMA, GSM,or WiMAX, as well as for “WiFi” connectivity using 802.11). Otherexamples are also possible.

The robotic system 100 may take on various forms. To illustrate,consider FIGS. 2A-2F showing example robots 200-210 (e.g., as conceptualgraphical representations) that may operate as robotic system 100discussed above. In particular, any of the robots 200-210 may beconfigured to operate according to a robot operating system (e.g., anoperating system designed for specific functions of the robot). A robotoperating system may provide libraries and tools (e.g., hardwareabstraction, device drivers, visualizers, message-passing, packagemanagement, etc.) to enable robot applications. Examples of robotoperating systems include open source software such as ROS (robotoperating system), DROS, or ARCOS (advanced robotics control operatingsystem); proprietary software such as the robotic development platformESRP from Evolution Robotics® and MRDS (Microsoft® Robotics DeveloperStudio), and other examples may also include ROSJAVA. A robot operatingsystem may include publish and subscribe functionality, and may alsoinclude functionality to control components of the robot, such as headtracking, base movement (e.g., velocity control, navigation framework),etc.

Robots 200 and 208 are shown as a mechanical form of a person includingarms, legs, and a head. Whereas, robots 202, 204, 206, and 210 includemechanical actuators comprising a base, wheels, and/or a motor. However,example robots 200-210 may take on any other form and may be configuredto receive any number of modules or components which may be configuredto operate the robot.

In an example implementation, robots 200-210 may obtain data from one ormore sensors 106. For example, a robot may take a picture of an objectand upload the picture to storage 104. An object recognition programused by the processor 102 may be configured to identify the object inthe picture and provide data about the recognized object, as well aspossibly about other characteristics (e.g., metadata) of the recognizedobject, such as a location, size, weight, color, etc.

In particular, robots 200-210 may include, store, or provide access to adatabase of information (e.g., as part of storage 104) related toobjects. The database may include information identifying objects, anddetails of the objects (e.g., mass, properties, shape, instructions foruse, etc., any detail that may be associated with the object) that canbe accessed by the robots 200-210 to perform object recognition (orfacial recognition during interaction with humans). As an example,information regarding use of an object can include, e.g., for a phone,how to pick up a handset, how to answer the phone, location of buttons,how to dial, etc.

In addition, the database may include information about objects (orhumans) that can be used to distinguish objects (or humans). Forexample, the database may include general information regarding anobject (e.g., such as a computer), and additionally, informationregarding a specific computer (e.g., a model number, details ortechnical specifications of a specific model, etc.). Each object mayinclude information in the database including an object name, objectdetails, object distinguishing characteristics, etc., or a tuple spacefor objects that can be accessed. Each object may further includeinformation in the database in an ordered list, for example.

In further examples, the database may include a global unique identifier(GUID) for objects (or humans) identified in the database (e.g., toenable distinguishing between specific objects/humans), and the GUID maybe associated with any characteristics or information describing theobject. Thus, a robot may be configured to access the database toreceive information generally distinguishing objects (e.g., a baseballvs. a computer), and to receive information that may distinguish betweenspecific objects (e.g., two different computers). Other examples mayalso be possible.

The robots 200-210 may perform any number of actions within an area,with people, with other robots, etc. In one example, each robot has WiFior another network based connectivity and may communicate with otherrobots directly or may upload/publish data to a cloud service that canthen be shared with any other robot. In this manner, the robots 200-210may share experiences with each other to enable learned behaviors. Forinstance, the robot 204 may traverse a pathway and encounter anobstacle, and can inform the other robots of a location of the obstacle.In another instance, the robot 204 can download data indicating imagesseen by the other robots to help the robot 204 identify an object usingvarious views (e.g., in instances in which one or more of the otherrobots have captured images of the objects from a differentperspective).

In still another example, the robot 208 may build a map of an area, andthe robot 204 can download the map to have knowledge of the area.Similarly, the robot 206 could update the map created by the robot 208with new information about the area (e.g., the hallway now has boxes orother obstacles), or with new information collected from sensors thatthe robot 208 may not have had (e.g., the robot 206 may record and addtemperature data to the map if the robot 408 did not have a temperaturesensor). Overall, the robots 200-210 may be configured to share datathat is collected to enable faster adaptation, such that each robot canbuild upon a learned experience of a previous robot.

Sharing and adaptation capabilities enable a variety of applicationsbased on a variety of inputs/data received from the robots 200-210. In aspecific example, mapping of a physical location, such as providing dataregarding a history of where a robot has been, can be provided. Anothernumber or type of indicators may be recorded to facilitatemapping/navigational functionality of the robots 200-210 (e.g., a scuffmark on a wall can be one of many cues that a robot may record and thenrely upon later to orient itself).

In an example implementation, a robot may include an integrateduser-interface (UI) that allows a user to interact with the device. Forexample, robots 200-210 may include various buttons and/or a touchscreeninterface that allow a user to provide input. As another example, therobots 200-210 may include a microphone configured to receive voicecommands from a user. Furthermore, the robots 200-210 may include one ormore interfaces that allow various types of user-interface devices to beconnected to the robot.

To illustrate, consider example robot 202 shown in FIG. 2B. The robot202 includes an on-board computing system, device 212, mechanicalactuator 214, and one or more sensors. In some examples, the robot 202may be configured to receive the device 212 that includes the processor102, the storage 104, and the sensors 106. For instance, the robot 202may be a robot that has a number of mechanical actuators (e.g., amovable base), and the robot may be configured to receive a mobiletelephone, smartphone, tablet computer, etc. to function as the “brains”or control components of the robot. The device 212 may be considered amodule of the robot. The device 212 may be physically attached to therobot. For example, a smartphone may sit on a robot's “chest” and forman interactive display. The device 212 may provide a robot with sensors,a wireless link, and processing capabilities, for example.

In particular, the robot 202 may be a toy with only limited mechanicalfunctionality, and by connecting device 212 to the robot 202, the robot202 may now be capable of performing a number of functions with the aidof the device 212. In this manner, the robot 202 (or components of arobot) can be attached to, for instance, a mobile phone to transform themobile phone into a robot (e.g., with legs/arms) that is connected to aserver to cause operation/functions of the robot.

In some examples, the device 212 may not be physically attached to therobot 202, but may be coupled to the robot 202 wirelessly. For example,a low cost robot may omit a direct connection to the internet. Thisrobot may be able to connect to a user's cellular phone via a wirelesstechnology (e.g., Bluetooth) to be able to access the internet. Therobot may be able to access various sensors and communication means ofthe cellular phone. The robot may not need as many sensors to bephysically provided on the robot, however, the robot may be able to keepthe same or similar functionality.

Thus, the robot 202 may include mechanical robot features, and may beconfigured to receive the device 212 (e.g., a mobile phone, smartphone,tablet computer, etc.), which can provide additional peripheralcomponents to the robot 202, such as any of an accelerometer, gyroscope,compass, GPS, camera, WiFi connection, a touch screen, etc., that areincluded within the device 212.

III. Example Robot to Human Feedback

FIG. 3 is a flowchart illustrating a method 300, according to an exampleimplementation. Illustrative methods, such as method 300, may be carriedout in whole or in part by a component or components in a roboticsystem, such as by the one or more of the components of the roboticsystem 100 shown in FIG. 1, and/or by the one or more of the componentsof the robots 200-210 shown in FIGS. 2A-2F. However, it should beunderstood that example methods, such as method 300, may be carried outby other entities or combinations of entities (i.e., by other computingdevices and/or combinations of computing devices), without departingfrom the scope of the invention.

As shown by block 302, method 300 involves determining, by a roboticsystem, a model of an environment in which the robotic system isoperating.

In one aspect, determining a model of the environment in which therobotic system is operating (i.e., evaluating surroundings of therobotic system) may involve receiving sensor data from one or moresensors (e.g., sensors 106) associated with the robotic system. Suchsensor data may include: image data, sound data, temperature data, depthdata, proximity data, motion data, speech recognition data, facialrecognition data, and/or location data, among other possibilities.

Given the sensor data, the robotic system may interpret the data into anunderstanding of objects, human faces, and gestures, as well as varioussituations in the environment. Additionally, the robotic system may beable to interpret how carrying out the task (or other interactions) mayimpact the environment (e.g., objects and/or humans in the environment)in which the robotic system is operating. As a result, such aninterpretation of the obtained sensor data may influence a state of therobotic system (e.g., a current model or interpretation of theenvironment), intention (e.g., a planned behavior or action of therobotic system), and/or overall safety of interaction (e.g., cautionwhile carrying out a task).

In an additional aspect, the robotic system may be configured to engagein one or more feedback modes as a result of the robotic system'sinteraction with the environment. Such feedback modes may be used toportray the state of the robotic system, a future intended action of therobotic system, and/or provide information (e.g., a warning) to a human,among other options. In one case, the robotic system may operate usingone or more visual indicators (e.g., light) such as LEDs or projectors,among other possibilities. This may specifically involve using one ormore light sources to emit light having particular lightcharacteristics. For instance, as illustrated in FIG. 2A, a robot 200may include a visual indicator 216 (e.g., in the form of a light bulb oran LED) configured to emit light (as illustrated by the short lines inthe figure) to provide feedback to a human. In another instance, asillustrated in FIG. 2E, robot 208 may include a projector 218 configuredto emit a projection 220 onto a surface that provides feedback to ahuman (e.g., in the form of an image). Other instances may also bepossible.

Visual indicators may provide feedback in various ways. For example,color or brightness of an LED may indicate a potential warning to ahuman. As further discussed below, this may be used in a situation whenthe robotic system is aware of proximity of the human to the roboticsystem while the robotic system is carrying out a task. In anotherexample, the robotic system may project lights of varying colors thatindicate an intended direction of motion and/or a gaze direction of therobotic system. In this example, the projection may indicate an objectthe robotic system is analyzing and/or may indicate an object that therobotic system intends to grasp or otherwise manipulate. Other examplesmay also be possible.

In another case, the robotic system may operate using auditoryindicators such as various sounds emitted from one or more speakers. Forinstance, as illustrated in FIG. 2F, a robot 210 may emit sounds thatprovide auditory feedback, such as by operating an auditory indicator222 (e.g., a speaker positioned in the head of robot 210). In oneexample, the sounds may include noises used as warning signs during apotentially dangerous situation. In another example, the sounds mayinclude commands that can be used as requests from a robotic system to ahuman (e.g., requesting that the human move away from an intended pathof the robotic system). In yet another example, the sounds may includestatements used as indicators of a current task the robotic system isperforming (e.g., a robotic system picking up an object may beprogrammed to provide an auditory statement such as “I am picking up anobject”). Other examples may also be possible.

In yet another case, the robotic system may use movement as a mode toprovide feedback to humans. For example, a robotic system (e.g., ahumanoid robot) may use a robotic arm to portray various gestures to ahuman. For instance, as illustrated in FIG. 2D, a robot 206 may use ahand gesture 224 by moving the robotic arm side to side (i.e.,“waving”), such as to attract attention of a human in the environment.Others gestures may include: pointing in the direction of a potentiallydangerous situation, moving a robot head side to side to indicate a “no”gesture or move the robot head up and down to indicate a “yes” gesture,and/or using the robotic arm for a “thumbs up” or a “thumbs down”gesture, among others. In another example, as further discussed below,the robotic system may move to another position in order to maximizesafety of the human, such as repositioning between a human and a firehazard. Other examples may also be possible.

In some cases, a robotic system may provide feedback to a human byinteracting with devices (e.g., via a wireless link as discussed above),such as a laptop or a smartphone associated with the human. Consider ascenario where a robotic system is looking for an object and is unableto find the object. The robotic system may need assistance in findingthe object but may recognize that the human is partaking in aconversation. Upon recognizing the situation, the robotic system maydetermine that using visual and auditory indicators may interrupt thehuman's conversation. As such, the robotic system may choose to requestassistance by sending a notification (e.g., SMS) to the human's mobiledevice. Other cases may also be possible.

In an example implementation, the robotic system may use multiple modesfor feedback simultaneously. For example, in a scenario where therobotic system is trying to attract attention of a human, the roboticsystem can simultaneously provide gestures (e.g., waving the roboticarm) and auditory signals such as lights. In particular, differentcombinations of feedback may indicate different states of the roboticsystem. For example, a waving motion combined with a red light outputmay provide an indication that the robotic system is in an emergencysituation. In contrast, a waving motion combined with a green lightoutput may provide an indication that the robotic system is trying toattract attention of a human in a non-emergency situation. Otherexamples may also be possible.

Note that such light indicators may be placed on a robot head (e.g., asshown in FIG. 2A), among other possible locations. The head may alsoinclude speakers, microphones, and/or panels. In some implementations,the head of the robotic system may include a tablet (e.g., device 212 inFIG. 2B) used to portray expressions, lights, and/or a task status(i.e., progress), among others. Additionally, the tablet may display avideo feed showing the environment from the perspective of the roboticsystem.

As shown by block 304, method 300 involves determining, by the roboticsystem, one or more of a state or intended operation of the roboticsystem based at least in part on the model of the environment.

Given a model of the environment, the robotic system may determine astate of the robotic system, such as progress of completing a task or acertain determination for instance. Additionally or alternatively, therobotic system may determine an intended operation, such as a plannedtask or planned trajectory for movement of an object for instance. Insome cases, this may involve determining a performance metric based atleast in part on the model of the environment, such as a performancemetric that may be associated with a level of risk of carrying out atask in the environment or may be associated with a level of risk of asituation interpreted from the model of the environment, among others.

In an example implementation, determining a performance metric that isassociated with a level of risk (or safety/concern) of a situation inthe environment and/or a task that the robotic system is carrying out inthe environment could be done in various ways. For example, the roboticsystem may process obtained sensor data from various sensors and mayinterpret the data to determine the performance metric. Note that theperformance metric may be in the form a number or other possiblevalues/indicators interpretable by a processor of the robotic system.Additionally, note that a level of safety may correspond to safety ofthe robotic system during the state/intended operation of the roboticsystem, safety of a human in the environment during the state/intendedoperation of the robotic system, and/or safety of an object in theenvironment during the state/intended operation of the robotic system,among others.

More specifically, varying data from a given sensor may result invarying values for a performance metric. In other words, specific datafrom specific sensors may include a corresponding performance metricstored in a database (e.g., storage 104). However, in someimplementation, the performance metrics may update over time based onlearned experiences of the robotic system.

Various cases will now be introduced to illustrate how a performancemetric may be determined from obtained sensor data. Note that the casesare discussed for illustration purposes and are not meant to be limitingas other example cases may also be possible without departing from thescope of the invention. Additionally, note that data from each sensormay result in a different performance metric and the various performancemetric may then be combined in any manner (e.g., each may be weighteddifferently) to result in an overall performance metric representing alevel of risk in the environment (and/or of a task).

In one case, temperature data indicating a high temperature, such as130° F., may correspond with a high value performance metric (e.g., a 9on a scale of 10). In particular, such a high value performance metricmay correspond with a high level of risk/concern (or a low level ofsafety) because such a high temperature may be harmful to components ofthe robotic system and/or to a human in the environment. In contrast,temperature data indicating an average temperature, such as 70° F., maycorrespond with a low value performance metric (e.g., a 1 on a scale of10). In particular, such a low value performance metric may correspondwith a low level of risk/concern (or a high level of safety) becausesuch an average temperature may not be harmful to components of therobotic system and/or to a human in the environment.

In another case, image data may be processed by the robotic system andvarious image matching techniques may be used to interpret theenvironment. Various images may be stored in a database of the roboticsystem (or on a cloud-based service) and each image may correspond to aperformance metric. For example, if the robotic system interprets imagedata that indicates a sharp object, such image data may correspond witha high value performance metric (i.e., high level of risk/concern)because the sharp object may be harmful to a human in the environment.In contrast, if the robotic system interprets image data that indicatesa round object, such image data may correspond with a low valueperformance metric (i.e., low level of risk/concern) because the roundobject may not be harmful to a human in the environment.

In yet another case, proximity data may be used to indicate a distancebetween the robotic system and a human while the robotic system iscarrying out a dangerous task. For example, the robotic system maydetermine a distance of 1 meter. Such a distance may correspond with ahigh value performance metric (i.e., high level of risk/concern) becausethe distance may put the human in a dangerous position (e.g., due to apotential collision with the robotic system). However, if the samedistance (i.e., 1 meter) is determined while the robotic system iscarrying out a non-dangerous task, the distance may correspond to alower value performance metric. In contrast, the robotic system maydetermine a distance of 10 meters. Such a distance may correspond with alow value performance metric (i.e., low level of risk/concern) becausethe distance may put the human in a non-dangerous position (e.g., nopotential for collision with the robotic system).

In yet another case, location data may be used to indicate whether acurrent location of the robotic system corresponds to a safe location.For example, the robotic system may determine that a current locationcorresponds to a home of the user of the robotic system. Such a locationmay correspond with a low value performance metric (i.e., low level ofrisk/concern) because the robotic system may be preconfigured todetermine that the home corresponds to a safe location for the roboticsystem and the user. In contrast, the robotic system may determine thata current location corresponds to remote unknown location. Such alocation may correspond with an average value performance metric (i.e.,average level of risk/concern) because the robotic system may bepreconfigured to determine that a remote unknown location may correspondto an unsafe location for the robotic system. Other cases may also bepossible.

As shown by block 306, method 300 involves, based at least in part onone or more of the state or the intended operation, making a selection,by the robotic system, of one or more of visual feedback to representone or more of the state or the intended operation, auditory feedback torepresent one or more of the state or the intended operation, and one ormore movements to represent one or more of the state or the intendedoperation.

Upon determining the state/intended operation based on the model of theenvironment, the robotic system may select an operating mode based onthe state/intended operation. The operating mode may include one of thefeedback modes discussed above such as a visual indicator, an auditoryindicator, a gesture, and/or a notification to a computing device.Additionally or alternatively, the operating mode may include one ormore movements. In one example, the one or more movements may involverepositioning of the robotic system from a first location to a secondlocation (e.g., to avoid a collision with a human). In another example,the one or more movement may involve repositioning of an object from afirst location to a second location (e.g., if the object is positionedin a location that is dangerous to the human). Other examples may alsobe possible.

Note that, in some implementations, the one or more movements mayessentially provide feedback and may thus be considered as a feedbackmode. However, in other implementations, the one or more movements maybe considered as separate from the feedback modes. For instance, variousfeedback modes (e.g., visual or auditory) may be used to provide awarning to a human while the one or more movements may be used aspreventative actions by the robot (e.g., avoiding a collision) if thewarning was not sufficient.

In an example implementation, each determined performance metric mayhave a corresponding operating mode indicated in the database (e.g., instorage 104 or in a cloud-based service). Upon determining theperformance metric, processor 102 may select the corresponding operatingmode. For example, the robotic system may determine a high levelperformance metric (i.e., a high level of risk) with a value of 8 on ascale of 10. Subsequently, the robotic system may determine that theoperating mode corresponding to such a high level performance metricincludes simultaneous operation of a visual indicator (e.g., a redlight) and an auditory indicator (e.g., a warning command). In anotherexample, the robotic system may determine a low level performance metric(i.e., a low level of risk) with a value of 2 on a scale of 10.Subsequently, the robotic system may determine that the operating modecorresponding to such a low level performance metric includes operationof a visual indicator (e.g., a green light). Other examples may also bepossible.

In a further aspect, determining the performance metric may also includean evaluation of the context of a situation in the environment (or atask carried out in the environment). As a result, selecting anoperating mode may also involve a consideration of the context inaddition to a value (i.e., level) of the performance metric that isassociated with the level of risk. In particular, different situations(or tasks) in the environment may result in the same determined valuefor the performance metric. However, different operating modes may beappropriate for different situations.

For example, a robotic system may determine a gaze direction of a human(e.g., using facial recognition techniques) during a dangeroussituation. In particular, the robotic system may determine that thedangerous situation corresponds to a high level performance metric(i.e., a high level of risk). However, if the robotic system determinesthat the gaze direction of the human is in the direction of the roboticsystem (i.e., a first context), the robotic system may select anoperating mode that includes, for instance, operation of a visualindicator (e.g., blinking red lights) as well as a gesture. This may bedue to stored information indicating that a gaze direction in thedirection of the robotic system may allow the human to see the warningfrom the robotic system in the form of light or gestures.

Whereas, if the robotic system determines that the gaze direction of thehuman is away from the location of the robotic system (i.e., a secondcontext), the robotic system may select an operating mode that includes,for instance, operation of an auditory indicator (e.g., a siren) as wellas a notification to a computing device. This may be due to storedinformation indicating that a gaze direction away from the location ofthe robotic system may not allow the human to see a visual warning fromthe robotic system and may thus require a different warning to get theattention of the human. Other examples may also be possible.

In this manner, the database of the robotic system may include, forinstance, a listing of various contexts for different possiblesituations (or tasks) in the environment and each situation may includevarying levels of risk/concern/safety. As a result, the performancemetric may be determined to include a context of a situation as well asa level of risk associated with the situation and the robotic system maysubsequently query the database to select the appropriate operating modecorresponding to the determined performance metric.

As shown by block 308, method 300 involves, based at least in part onthe selection, engaging, by the robotic system, in one or more of thevisual feedback to represent one or more of the state or the intendedoperation, the auditory feedback to represent one or more of the stateor the intended operation, and the one or more movements to representone or more of the state or the intended operation.

Various example scenarios will now be introduced to illustrate howmethod 300 may be used. Note that the scenarios are discussed forillustration purposes and are not meant to be limiting as other examplescenarios may also be possible without departing from the scope of theinvention.

FIG. 4A illustrates robot 204 operating in an environment as well as ahuman 402 positioned in the vicinity of the robot 204 (e.g., within athreshold distance). Additionally, FIG. 4A illustrates regions400A-400C. Region 400A may be an area where the robot 204 is carryingout a dangerous task and may be unsafe (i.e., a low level of safety) forthe human 402. Region 400B may be adjacent to region 400A and maycorrespond with an average level of safety for the human 402. Incontrast, region 400C may be a sufficient distance away from region 400Aand may be safe (i.e., a high level of safety) for the human 402.

In an example implementation, the robot 204 may be configured to predictone or more actions by the human 402. The robot 204 may then alsodetermine the performance metric based on the predicted action (e.g., inaddition to the possible factors discussed above). For instance, therobot 204 may determine a gaze direction 404 of the human 402. Based onthe gaze direction 404, the robot 204 may predict that the human 402 iswalking in the direction of region 400A and may determine a performancemetric based on the prediction. In order to warn the human 402, therobot 402 may operate in a feedback mode that is selected based on theperformance metric. For example, as illustrated in FIG. 4A, the robotmay operate a visual indicator 216 (e.g., a green light) while the human402 is positioned in region 400C (i.e., a safe region).

In an additional aspect, the robot 204 may be configured to evaluate oneor more actions by the human 402. The robot 204 may then also determinethe performance metric based on the evaluated action (e.g., in additionto the possible factors discussed above). For instance, as illustrate inFIG. 4B, the robot 204 may evaluate that the human 402 moved from region400C to region 400B. Based on such an evaluation, the robot 204 maydetermine that the performance metric changed from a value correspondingto a high level safety to a value corresponding to an average level ofsafety.

Due to the change in the level of safety, the robot 204 may change thefeedback mode. For example, as illustrated in FIG. 4C, the robot 204 maychange from operating a visual indicator 216 to operating an auditoryindicator 222 (e.g., the robot may emit auditory feedback indicating“warning! I am carrying out a dangerous task!”). As shown in FIG. 4C,such an auditory indicator 222 may get the attention of the human 402and may allow the human 402 to take further action such as to avoidentering region 400A based on knowledge of the situation received fromthe auditory indicator 222.

Consider a situation where human 402 moves from region 400B to 400A(e.g., regardless of the previous warnings). The robot 204 may evaluatethat the human 402 moved from region 400B to region 400A. Based on suchan evaluation, the robot 204 may determine that the performance metricchanged from a value corresponding to an average level of safety to avalue corresponding to a low level of safety.

Due to the change in the level of safety, the robot 204 may change theoperating mode. In one example, the robot 204 may engage in a differentfeedback mode, such as operating a visual indicator 216 (e.g., ablinking red light) while simultaneously operating an auditory indicator222 (e.g., a loud siren). In another example, as illustrated in FIG. 4D,the robot 204 may engage in one or more movement such as relocating froma first location to a second location. For instance, as shown in FIG.4D, robot 204 may relocate from region 400A to region 400B and maycontinue carrying out the dangerous task in region 400B. Due to therobot 204 relocating from region 400A to region 400B, region 400B maynow be unsafe (i.e., a low level of safety) for the human 402 whileregion 400A may now correspond with an average level of safety for thehuman 402.

In this manner, a robotic system may determine a performance metricbased on a model of the environment, where the performance metric may beassociated with a first level of safety (or risk/concern) of carryingout a task in the environment and/or of a situation in the environment.The robotic system may then determine that the first level of safety isabove a threshold level of safety. Such a threshold level of safety mayinvolve, for instance, crossing from region 400C to region 400B whilethe first level of safety may involve, for instance, the human 402positioned in region 400C (i.e., a region safer than region 400B). Assuch, based on determining that the first level of safety is above thethreshold level of safety, the robotic system may engage in a firstfeedback mode (e.g., operating the visual indicator 216 as show in inFIG. 4A).

In some cases, the robotic system may determine that the first level ofsafety changed to a second level of safety, where the second level ofsafety is below the threshold level of safety. For instance, as shown inFIG. 4B, the human 402 may have crossed the threshold level of safety bymoving from region 400C to region 400B and may now be positioned inregion 400B (i.e., corresponding to a lower level of safety). As such,based on determining that the first level of safety changed to thesecond level of safety, the robotic system may engage in a secondfeedback mode (e.g., operating the auditory indicator 222 as show in inFIG. 4C).

In another case, the threshold level of safety may involve, forinstance, crossing from region 400B to region 400A while the first levelof safety may involve, for instance, the human 402 positioned in region400B (i.e., a region safer than region 400A). Additionally, the secondlevel of safety may involve the human 402 positioned in region 400A. Asshown in FIG. 4D, the human 402 may have crossed the threshold level ofsafety by moving from region 400B to region 400A and may now bepositioned in region 400A (i.e., corresponding to a lower level ofsafety). As such, based on determining that the first level of safetychanged to the second level of safety, the robotic system may engage inone or more movement such that the level of safety is increased to alevel that is above the threshold level of safety (e.g., as discussedabove in association with FIG. 4D).

Note that, in some implementations, the robotic system may not considerthresholds as discussed above. For instance, if the selected operatingmode includes the one or more movements, the robotic system may engagein the one or more movement such that a distance between the roboticsystem and an object changed from a first distance to a second distance,where the first distance may correspond to a first level of risk and thesecond distance may correspond to a second level of risk that is lowerthan the first level of risk. In this manner, the robotic system mayreposition such that the first level of risk is reduced to the secondlevel of risk without a consideration of thresholds. Other instances mayalso be possible.

FIG. 5A illustrates robot 204 operating in an environment as well ashuman 502 (e.g., an adult) positioned in the vicinity of the robot 204.Additionally, FIG. 5A illustrates a sharp object 504 in the environmentas well as an intended path 506A of the human 502. As illustrated, theintended path 506A of the human 502 intersects with the sharp object504. As such, if the human 502 continues moving in the direction of theintended path 506A, the human 502 may step on the sharp object 504 andmay get hurt.

Robot 204 may evaluate the surroundings and use, for instance, objectrecognition techniques to determine that the object 504 is a sharpobject. Additionally, the robot 204 may use proximity data to determinea distance between the robot 204 and the sharp object 504, a distancebetween the robot 204 and the human 502, and/or a distance between thehuman 502 and the sharp object 504. Further, the robot 204 may usefacial recognition techniques to determine a gaze direction of the human502 and estimate the intended path 506A of the human 502. Yet further,the robot 204 may use motion data to determine a speed at which thehuman 502 is moving.

Using this information the robot 204 may determine that the sharp object504 is located in the intended path 506A of the human 502. Also, therobot 204 may use this information (e.g., the motion data) to determinean estimated time when the human 502 may step on the sharp object 504.Based on such information, the robot 204 may determine a model of theenvironment and then use the model to determine a performance metricassociated with the level of risk of the situation. Given theperformance metric, the robot 204 may then select an operating mode thatmay allow the robot 204 to warn the human 502 that the sharp object 504is located in the intended path 506A of the human 502.

As illustrated in FIG. 5B, the robot 204 may select to operate anauditory indicator 222. The auditory indicator 222 may be, for example,a loud siren and/or a statement such as: “Beware! A sharp object is inyour intended path!” In another example, the robot 204 may additionallyoperate a visual indicator such as by projecting a light in thedirection of the sharp object 504 (e.g., as shown in FIG. 2E). In yetanother example, the robot 204 may simultaneously operate an auditoryindicator and a visual indicator, among other possible feedback modes.

In a further aspect, robot 204 may use various techniques such as speechrecognition to determine, for instance, a language spoken by the human502. If the robot 204 determines that the human 502 is an Englishspeaking adult, the robot may use an auditory indicator 222 thatincludes a verbal warning in the English language. In contrast, if therobot 204 determines that the human 502 is a Spanish speaking adult, therobot 204 may use an auditory indicator 222 that includes a verbalwarning in the Spanish language.

In an example implementation, if the human 502 continues walking in theintended path 506A, the robot 204 may determine that the performancemetric changes to a metric associated with a higher level of risk as thedistance between the human 502 and the sharp object 504 gets shorter.Based on the change in the performance metric, the robot 204 may selectdifferent operating modes as the human 502 gets closer to the sharpobject 504 (e.g., in similar manner to the selection of operating modesdiscussed above in association with FIG. 4A-4D).

However, as illustrated in FIG. 5C, the robot 204 may determine that theintended path 506A of the human 502 changed to a different intended path506B. The robot 204 can make such a determination based on, forinstance, determining a change in the gaze direction of the human 502and/or determining a change in the body orientation of the human 502(e.g., using motion data), among other options. The robot 204 may thendetermine that the new intended path 506B does not intersect with thesharp object 504 (i.e., the object 504 is now located away from theintended path 506B).

Upon making such a determination, the performance metric may change to ametric associated with a lower level of risk and the robot 204 may haltengaging in the selected operating mode as illustrated in FIG. 5C.Alternatively, the robot 204 may select a different operating mode toindicate to the human 502 that the intended path 506B of the human 502no longer intersects with the sharp object 504. For example, the robot204 may operate an auditory indicator that includes a statement such as:“No worries! The sharp object is no longer in your intended path!”

In a similar manner to FIGS. 5A-5C, FIG. 5D illustrates robot 204operating in an environment as well as a child 508 positioned in thevicinity of the robot 204. Additionally, FIG. 5D illustrates the sharpobject 504 in the environment as well as an intended path 510A of thechild 508. As illustrated, the intended path 510A of the child 508intersects with the sharp object 504. As such, if the child 508continues moving in the direction of the intended path 510A, the child508 may step on the sharp object 504 and may get hurt.

Robot 204 may use, for instance, facial recognition techniques todetermine (or estimate) an age of a human. A performance metricdetermined by the robot 204 may then also be based on the age of thehuman. In particular, as mentioned above, a performance metric mayinclude a context of a situation. Such a context may be the age of thehuman, where interaction with the human may depend on the age of thehuman. As such, when selecting an operating mode, the robot 204 may beconfigured to select an operating mode that is appropriate based on theage of the human.

For example, FIGS. 5A-5C illustrated an interaction with an adult, wherethe robot 204 selected to operate an auditory indicator 222 to warn theadult about the sharp object 504. In contrast, as illustrated in FIG.5E, the robot 204 may select an operating mode that includes one or moremovements based on a determination that the human is a child 508. Inparticular, the robot 504 may reposition the sharp object 504 from afirst location that is in the intended path 510A of the child 508 to asecond location that is away from the intended path 510A of the child508. In this manner, the level of risk of the child 508 getting hurt isreduced.

Note that the robot 204 may be configured to select such an operatingmode because the child 508 may be very young (e.g., a 5 year old) andmay not understand auditory feedback as well as an adult may understandthe auditory feedback, among other possible reasons.

In yet another example scenario, the robotic system may determine anarea of interest within the environment, such as a particular object forinstance. Once the robotic system determine the area of interest, therobotic system may responsively determine that a task should be carriedout related to this area of interest, such as grasping onto the objectfor instance. Then, once the robotic system determines the task, therobotic system may select a feedback mode such as by making a selectionof visual feedback that includes projection of light from one or morelight sources of the robotic system towards the area of interest. Afterthe selection is made, the robotic system may then project the lightfrom the one or more light sources of the robotic system towards thearea of interest. Other example scenarios may also be possible.

IV. Conclusion

The present disclosure is not to be limited in terms of the particularimplementations described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its spirit and scope, as will be apparentto those skilled in the art. Functionally equivalent methods andapparatuses within the scope of the disclosure, in addition to thoseenumerated herein, will be apparent to those skilled in the art from theforegoing descriptions. Such modifications and variations are intendedto fall within the scope of the appended claims.

The above detailed description describes various features and functionsof the disclosed systems, devices, and methods with reference to theaccompanying figures. In the figures, similar symbols typically identifysimilar components, unless context dictates otherwise. The exampleimplementations described herein and in the figures are not meant to belimiting. Other implementations can be utilized, and other changes canbe made, without departing from the spirit or scope of the subjectmatter presented herein. It will be readily understood that the aspectsof the present disclosure, as generally described herein, andillustrated in the figures, can be arranged, substituted, combined,separated, and designed in a wide variety of different configurations,all of which are explicitly contemplated herein.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other implementations can includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements can be combined or omitted. Yet further, anexample implementation can include elements that are not illustrated inthe figures.

While various aspects and implementations have been disclosed herein,other aspects and implementations will be apparent to those skilled inthe art. The various aspects and implementations disclosed herein arefor purposes of illustration and are not intended to be limiting, withthe true scope being indicated by the following claims.

I claim:
 1. A method comprising: determining, by a robotic system, amodel of an environment in which the robotic system is operating;determining, by the robotic system, one or more of a state or intendedoperation of the robotic system based at least in part on the model ofthe environment; determining, by the robotic system, a first performancemetric that is associated with a first level of safety of one or more ofthe state or the intended operation; determining, by the robotic system,that the first performance metric is above a threshold performancemetric; based at least in part on determining that the first performancemetric is above the threshold performance metric, making a selection, bythe robotic system, of a first feedback mode corresponding to the firstlevel of safety; based at least in part on the selection, engaging, bythe robotic system, in the first feedback mode; determining, by therobotic system, that the first performance metric changed to a secondperformance metric that is associated with a second level of safety, andresponsively determining that the second performance metric is below thethreshold performance metric; based at least in part on determining thatthe second performance metric is below the threshold performance metric,making a further selection, by the robotic system, of a second feedbackmode corresponding to the second level of safety; and based at least inpart on the further selection, engaging, by the robotic system, in thesecond feedback mode.
 2. The method of claim 1, wherein engaging in thesecond feedback mode comprises using one or more light sources to emitlight having particular light characteristics.
 3. The method of claim 1,wherein determining the model of the environment comprises determiningan area of interest in the environment, wherein determining one or moreof a state or intended operation comprises determining one or more of astate or intended operation related to the area of interest, whereinmaking the further selection of the second feedback mode comprisesselecting visual feedback that includes projection of light from one ormore light sources of the robotic system towards the area of interest,and wherein engaging in the second feedback mode comprises projectingthe light from the one or more light sources of the robotic systemtowards the area of interest.
 4. The method of claim 1, wherein thefirst feedback mode comprises one or more of first visual feedback torepresent the first level of safety, first auditory feedback torepresent the first level of safety, and one or more first movements torepresent the first level of safety, and wherein the second feedbackmode comprises one or more of second visual feedback to represent thesecond level of safety, second auditory feedback to represent the secondlevel of safety, and one or more second movements to represent thesecond level of safety.
 5. The method of claim 1, wherein determiningthe model of the environment comprises determining a first distancebetween the robotic system and an object positioned in the environment,wherein determining that the first performance metric is above athreshold performance metric comprises determining that the firstdistance is greater than a threshold distance, wherein determining thatthe first performance metric changed to a second performance metriccomprises determining that a distance between the robotic system and theobject changed from the first distance to a second distance, and whereindetermining that the second performance metric is below the thresholdperformance metric comprises determining that the second distance islesser than the threshold distance.
 6. The method of claim 1, furthercomprising: based at least in part on the model of the environment,predicting, by the robotic system, one or more actions by a humanpositioned within a threshold distance of the robotic system, whereindetermining one or more of a state or intended operation of the roboticsystem is further based at least in part on the predicted one or moreactions.
 7. The method of claim 1, further comprising: based at least inpart on the model of the environment, evaluating, by the robotic system,one or more actions by a human positioned within a threshold distance ofthe robotic system, wherein determining one or more of a state orintended operation of the robotic system is further based at least inpart on the evaluated one or more actions.
 8. The method of claim 1,wherein engaging in the first feedback mode comprises sending anotification to a computing device.
 9. The method of claim 1, whereindetermining the model of the environment comprises receiving sensordata, from one or more sensors associated with the robotic system,indicating one or more of: image data, sound data, temperature data,depth data, proximity data, motion data, speech recognition data, facialrecognition data, and location data.
 10. The method of claim 1, whereinengaging in the second feedback mode comprises sending a notification toa computing device.
 11. A robotic system comprising: one or moreprocessors; a non-transitory computer readable medium; and programinstructions stored on the non-transitory computer readable medium andexecutable by the one or more processors to: determine a model of anenvironment in which the robotic system is operating; determine one ormore of a state or intended operation of the robotic system based atleast in part on the model of the environment; determine a firstperformance metric that is associated with a first level of safety ofone or more of the state or the intended operation; determine that thefirst performance metric is above a threshold performance metric; basedat least in part on determining that the first performance metric isabove the threshold performance metric, make a selection of a firstfeedback mode corresponding to the first level of safety; based at leastin part on the selection, engage in the first feedback mode; determinethat the first performance metric changed to a second performance metricthat is associated with a second level of safety, and responsivelydetermining that the second performance metric is below the thresholdperformance metric; based at least in part on determining that thesecond performance metric is below the threshold performance metric,make a further selection of a second feedback mode corresponding to thesecond level of safety; and based at least in part on the furtherselection, engage in the second feedback mode.
 12. The robotic system ofclaim 11, further comprising one or more appendages, wherein engaging inthe first feedback mode comprises carrying out one or more gesturesusing the one or more appendages.
 13. The robotic system of claim 11,wherein engaging in the second feedback mode comprises repositioning ofthe robotic system from a first location in the environment to a secondlocation in the environment.
 14. The robotic system of claim 11, whereinengaging in the second feedback mode comprises the robotic systemrepositioning an object in the environment from a first location in theenvironment to a second location in the environment.
 15. The roboticsystem of claim 14, wherein determining the model of the environmentcomprises determining an estimated path of a human in the environment,wherein the first location is in the estimated path of the human, andwherein the second location is away from the estimated path of thehuman.
 16. A non-transitory computer readable medium having storedtherein instructions executable by one or more processors to cause arobotic system to perform functions comprising: determining a model ofan environment in which the robotic system is operating; determining oneor more of a state or intended operation of the robotic system based atleast in part on the model of the environment; determining a firstperformance metric that is associated with a first level of safety ofone or more of the state or the intended operation; determining that thefirst performance metric is above a threshold performance metric; basedat least in part on determining that the first performance metric isabove the threshold performance metric, making a selection of a firstfeedback mode corresponding to the first level of safety; based at leastin part on the selection, engaging in the first feedback mode;determining that the first performance metric changed to a secondperformance metric that is associated with a second level of safety, andresponsively determining that the second performance metric is below thethreshold performance metric; based at least in part on determining thatthe second performance metric is below the threshold performance metric,making a further selection of one or more movements to increaseperformance to a level that is above the threshold performance metric;and based at least in part on the further selection, engaging in the oneor more movements to increase performance to a level that is above thethreshold performance metric.
 17. The non-transitory computer readablemedium of claim 16, wherein the first level of safety corresponds to oneor more of: safety of the robotic system during one or more of the stateor the intended operation, safety of a human in the environment duringone or more of the state or the intended operation, and safety of anobject in the environment during one or more of the state or theintended operation.
 18. The non-transitory computer readable medium ofclaim 16, the functions further comprising: based at least in part onthe model of the environment, estimating an age of a human in theenvironment, wherein making the selection is further based at least inpart on the estimated age of the human.
 19. The non-transitory computerreadable medium of claim 16, the functions further comprising: based atleast in part on the model of the environment, detecting a languagespoken by a human in the environment, wherein making the selectioncomprises selecting auditory feedback that includes speech in thelanguage spoken by the human.
 20. The non-transitory computer readablemedium of claim 16, wherein the first feedback mode comprises one ormore of first visual feedback to represent the first level of safety,first auditory feedback to represent the first level of safety, and oneor more first movements to represent the first level of safety.
 21. Thenon-transitory computer readable medium of claim 16, wherein engaging inthe first feedback mode comprises sending a notification to a computingdevice.
 22. The non-transitory computer readable medium of claim 16,wherein determining the model of the environment comprises determining afirst distance between the robotic system and an object positioned inthe environment, wherein the first distance corresponds to the firstperformance metric, and wherein engaging in the one or more movements toincrease performance to a level that is above the threshold performancemetric comprises repositioning the robotic system such that the firstdistance changes to a second distance corresponding with performancebeing at a level that is above the threshold performance metric.